Reprogramming FairGANs with Variational Auto-Encoders: A New Transfer Learning Model
This work addresses the challenge of making fairness-aware generative models more applicable and easier to use across different domains, representing an incremental advancement in transfer learning for fair AI.
The paper tackles the problem of transferring pre-trained fairness-aware GANs (FairGANs) to new tasks while preserving their fairness and utility goals, introducing a novel framework that uses Variational Auto-Encoders for this reprogramming process.
Fairness-aware GANs (FairGANs) exploit the mechanisms of Generative Adversarial Networks (GANs) to impose fairness on the generated data, freeing them from both disparate impact and disparate treatment. Given the model's advantages and performance, we introduce a novel learning framework to transfer a pre-trained FairGAN to other tasks. This reprogramming process has the goal of maintaining the FairGAN's main targets of data utility, classification utility, and data fairness, while widening its applicability and ease of use. In this paper we present the technical extensions required to adapt the original architecture to this new framework (and in particular the use of Variational Auto-Encoders), and discuss the benefits, trade-offs, and limitations of the new model.